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1. Field of Invention
This invention relates to computerized detection of abnormal anatomical regions depicted in radiographs.
2. Background
Detection and analysis of target objects in digital images are useful and important tasks. For example, detection and diagnosis of abnormal anatomical regions in radiographs, such as masses and microcalcifications in women""s breast radiographs (mammograms), are among the most important and difficult tasks performed by radiologists.
Breast cancer is a leading cause of premature death in women over forty years old. Evidence shows that early detection, diagnosis and treatment of breast cancer significantly improves the chances of survival, reducing breast cancer morbidity and mortality. Many methods for early detection of breast cancer have been studied and tested, among them mammography. To date, mammography has proven to be the most cost effective means of providing useful information to diagnosticians regarding abnormal features in the breast and potential risks of developing breast cancer in large populations. The American Cancer Society currently recommends the use of periodic mammography and screening of asymptomatic women over the age of forty with annual examinations after the age of fifty. Mammograms may eventually constitute one of the highest volume X-ray images routinely interpreted by radiologists.
Between thirty and fifty percent of breast cancers detected radiographically demonstrate clustered microcalcifications on mammograms, and between sixty and eighty percent of breast cancers reveal microcalcifications upon microscopic examination. Therefore, any increase in the detection of clustered microcalcifications by mammography may lead to further improvements in its efficiency in the detection of early breast cancer.
Currently, acceptable standards of clinical care are that biopsies are typically performed on five to ten women for each cancer removed. With this high biopsy rate is the reasonable assurance that most mammographically detectable early cancers will be resected. However, reducing the biopsy rate without adversely affecting health is desirable. Accordingly, given the large amount of overlap between the characteristics of benign and malignant lesions which appear in mammograms, computer-aided detection and/or diagnosis (CAD) of abnormalities may have a great impact on clinical care.
At present, mammogram readings are performed visually by mammographic experts, that is, physicians and radiologists. Unfortunately, visual reading of mammograms has two major disadvantages. First, it is often possible to miss the breast cancer in its early stages. This is because, unlike many other cancers, there is as yet no clear way to detect premalignant changes in the breast. This results partly from the relative inaccessibility of breast tissue. A second disadvantage of visual reading of mammograms is that these readings are labor intensive, time consuming, and subjective. Also, multiple readings of a single mammogram may be necessary in order to increase the reliability of the diagnosis.
Therefore, it would be advantageous and useful to have CAD systems to help radiologists and physicians obtain quicker, more consistent, and more precise results when performing visual readings of mammograms. Such CAD systems would aid in cancer detection and improve the efficiency and accuracy of large-scale screening.
During the past twenty years, an ever-increasing number of CAD systems for mammography have been developed and tested in an attempt to increase diagnostic accuracy and improve the efficacy and efficiency of mammographic interpretations. Current CAD schemes for mass detection typically can be partitioned into three stages. The first stage identifies suspicious regions using either single image segmentation or bilateral image subtraction; the second stage calculates a feature vector for each of these suspicious regions; and the third stage classifies regions based on some type of decision mechanism applied to feature vectors. Regions which are ultimately classified as positive by the above process can be marked on a copy of the original image for presentation to a radiologist or for use in other analysis.
Recent reports on advances in CAD applied to mass detection indicate that there are some generalizations that can be made about performance limitations of current methods.
In order to achieve a high true-positive detection rate (i.e., sensitivity greater than 90%), all of these schemes report a relatively large false-positive detection rate, even when testing a limited image database. It is not uncommon to produce a false-positive detection rate of one region per-image for clustered microcalcifications and two false-positive detections per image for masses. Previous attempts to improve early CAD schemes have employed many different techniques, but none of these efforts have been able to reduce the false positive rate to acceptable levels.
It is widely believed that current performance of CAD for mass detection is significantly less than that of radiologists, given the same task, though quantitative comparisons are difficult because radiologists rarely read single images without supplementary information. To directly compare radiologists"" performance with current CAD would require that radiologists be restricted to evaluating limited regions-of-interest on mammograms, in assessing the likelihood of a mass in the region.
In recent years, despite considerable effort by many groups, the rate of improvement in CAD performance has slowed to the point that performance statistics of the better systems seem to be approaching an asymptote. This performance level, which is largely independent of the specifics of implementation (e.g., neural networks, Bayesian networks or rule based systems), is believed to be well below the potential performance of CAD. The inventors of the present invention believe that a possible reason for this may be that essentially all current CAD implementations apply traditional paradigms of signal processing and pattern recognition to detect features in individual images, and it is probable that most of the relevant physical features, in single-views, have been identified and exploited to some extent. Whatever information remains untapped in single-views is either very elusive (i.e., difficult to program), at a higher level of abstraction, or has only a small potential impact on performance.
In contrast to CAD, mammographers routinely insist on concurrently reading multiple images (at least two of each breast) in evaluating cases. Apparently, a significant part of their decision process requires a synergistic interaction of multiple components of information, as opposed to evaluating each separately and then combining individual decisions. The degree to which each component of image information independently. affects the performance of radiologists, and potentially of CAD, is not known. A limited number of studies have shown that single-view mammography leads to a higher rate of recall, and results in a failure to detect 11% to 25% of cancers than would have been detected using multiple views. These observations, along with the fact that mammographers insist on comparing all views that are available, strongly suggests that they derive useful gains in performance from this.
Ipsilateral pairs of mammograms (i.e., two views of the same breast taken at some oblique angle) contain spatial information about a single breast. However, because the process of acquiring each image requires that the breast be compressed in a direction orthogonal to the image plane (flattened on the image detector), each image of the pair represents a different distortion of the breast tissue. Although it is not feasible to geometrically reconstruct a three dimensional model of the breast from this data, it is possible to derive certain kinds of information by comparing features between views. For example, if a mass appears in one view, it should appear in the second view as well, or there should at least be sufficient ambiguity in the second view to explain its absence. Although the exact location of a feature in the second view will be unknown, there are constraints on where it might appear based on its position in the first image and the geometric effects of breast compression. Many kinds of image features (e.g., location, texture, degree of spiculation, and integrated density difference) tend to be relatively invariant, or at least behave predictably, with respect to breast compression.
In one aspect, this invention is a method of detecting an abnormal region in living tissue. The method includes obtaining images from a different views of the living tissue; performing single-image CAD of each image to determine suspected abnormal regions depicted in the image; and combining measurements of the suspected abnormal regions in each image to determine whether a suspected abnormal region is an abnormal region.
In preferred embodiments, the living tissue is a human breast, the abnormal region is a mass in the breast, and the obtaining comprises obtaining ipsilateral mammographic views of the breast, preferably a craniocaudal view of the breast and a mediolateral oblique view of the breast.
In some embodiments, the single-image CAD of each image produces a feature vector for each of the various suspicious regions depicted in the images. In some preferred embodiments, the features are relatively invariant or behave predictably with respect to breast compression. The features may include one or more of a radial distance of the suspicious region from the nipple; a length of region projection parallel to the nipple axis line; an integrated contrast difference; a size of the suspicious region; and a measure of complexity of the region boundary.
In some embodiments, the combining of measurements comprises evaluating combinations of suspected abnormal regions from each view; and producing a single multi-view measurement for reach suspected abnormal region based on the measurements of each region from each view. In some embodiments, the multi-view measurement is defined to be the absolute value of the logarithm of the ratio of the corresponding single-image measurements. In some embodiments, the result of the combining is used to train the single-image CAD.
In another aspect, this invention is a method of detecting a mass in a human breast. The method includes obtaining ipsilateral mammographic views of the breast; for each image, performing CAD of the image to determine suspected masses depicted in the image; and combining measurements of the suspected masses in each image to determine whether a suspected abnormal region is a mass. In some embodiments, one image is from a craniocaudal view of the breast and the other image is from the mediolateral oblique view of the breast.
In some embodiments, the CAD of each image produces a feature vector of various suspicious regions depicted in the images. The features are preferably relatively invariant or behave predictably with respect to breast compression.